TTLSC – transductive total least square model for classification and its application in medicine

Authors:
Qun Song;Tian Min Ma;Nikola Kasabov
Affiliations:
Knowledge Engineering & Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand;Knowledge Engineering & Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand;Knowledge Engineering & Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand
Venue:
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Year:
2006

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Abstract

This paper introduces a novel classification method-transductive total least square classification method (TTLSC). While inductive approaches are concerned with the development of a model to approximate data in the whole problem space (induction), and consecutively – using this model to calculate the output value(s) for a new input vector (deduction), in transductive systems a local model is developed for every new input vector, based on some closest data to this vector from the training data set. The total least square method (TLS) is one of the optimal fitting methods that can be used for curve and surface fitting and outperform the commonly used least square fitting methods in resisting both normal noise and outlier. The TTLSC is illustrated by a case study: a real medical decision support problem of estimating the survival of haemodialysis patients. This personalized modelling can also be applied to solve other classification or clustering problems.